Abstract：This paper analyses review spam from the perspective of the usefulness of information, selects digital camera reviews as the research object and builds the data set, then from the three aspects of review, reviewer and product chooses 11 features, uses 4 different kernel functions in SVM model to identify review spam of products, optimizes the parameters C and γ of RBF that has a better identification, which improves accuracy rate of the identification effect of review spam to 78.16% and recall rate to 72.18%. By comparing the selected 4 different combinations of features, the authors find the combination of review, reviewer and product is the best. Finally, it proves that SVM is significantly better than other algorithms compared to the Logistic Regression.
李霄, 丁晟春. 垃圾商品评论信息的识别研究[J]. 现代图书情报技术, 2013, 29(1): 63-68.
Li Xiao, Ding Shengchun. Research on Review Spam Recognition. New Technology of Library and Information Service, 2013, 29(1): 63-68.
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